1. Brief Description
The present invention relates to a method of operating a computer network. It has particular utility in relation to peer-to-peer networks in which peers provide services to one another.
2. Description of Related Art
Until recently, the World-Wide Web has largely been used for providing information or content to users. However, the proportion of web-servers offering processing in addition to information is growing. The services offered in this way to the developers of distributed application programs must have defined interfaces so that the developers can program the computer they are programming to call upon the web server to execute a process remotely. This sort of remote execution is well known and was first developed in the form of remote procedure calls (RPC), a more flexible framework then being provided by the Common Object Request Broker Architecture (CORBA), and an even more flexible framework then being provided in the form of Web Services.
The selection of a web-service to form part of a distributed application program is often made by the programmer at design-time (i.e. the programmer hard codes the identity of the service provider in the code he generates). However, in scenarios where the network or the services providers are unstable, this is inflexible. Hence, it is known to provide code which causes the computer requesting the service to decide upon a service provider at run-time. Indeed, ‘late-binding’ like this is seen in Birrell and Nelson's seminal paper ‘Implementing Remote Procedure Calls’, ACM Transactions on Computer Systems, Vol. 2, No. 1, February 1984, Pages 39-59.
One type of such dynamic service selection utilises clients' past experiences of the quality of service provided by different servers. In many implementations, data representing past experiences are shared by each client with other clients. Often, this sharing is achieved by having each client post data representing its experience to a shared database accessible to other clients.
J. Day and R. Deters' paper “Selecting the Best Web Service” presented at the 14th Annual IBM Centers for Advanced Studies Conference, 2004 presents two methods by which a client may ‘reason’ about which service provider to select. One is a rule-based expert system, the other a naïve Bayes reasoner. The downside of deterministic service selection based on shared rankings—namely that the highest ranked service provider tends to be overloaded is recognised. The problem is said to be better dealt with by service selection using the naïve Bayes reasoner, since this classifies services into groups, one member from the group being chosen at random—this introducing a more probabilistic service selection which avoids overloading the highest-ranked provider. The possibility of distributing the performance data in a peer-to-peer like system is mentioned towards the end of the paper. Le-Hung Vu et al in “QoS-based Service Selection and Ranking with Trust and Reputation Management”, suggest that distributing performance data is ‘a bit unrealistic as each service consumer would have to take the heavy processing role of a discovery and reputation system’.
A similar problem is found in peer-to-peer networks which rely on reputation management to overcome the detrimental influence of malign peers. S. Kamvar, M. Schlosser, and H. Garcia-Molina's paper “Eigenrep: Reputation management in p2p networks”, Twelfth International World Wide Web Conference, 2003 proposes a two-fold approach to the problem:                i) with a one-in-ten probability, to try, at random, a peer which has not yet been tried; and, in the other nine-out-of-ten cases        ii) to make the service provider selection of each client probabilistic rather than deterministic—though the probability of selection is still higher the higher the ranking of the provider.        
In both cases, the solution can be seen to be to move from a deterministic service selection to a more probabilistic selection. For obvious reasons, neither proposes truly random selection since this would obviate the advantage of sharing quality-of-service (QoS) information in the first place.